Salesforce’s Agentforce Vibes 2.0 targets a hidden failure: context overload in AI agents
Source: VentureBeat
Overview
When startup fundraising platform VentureCrowd began deploying AI coding agents, they saw the same gains as other enterprises: they cut the front‑end development cycle by 90 % in some projects.
However, it didn’t come easy or without a lot of trial and error.
VentureCrowd’s first challenge revolved around data and context quality. Diego Mogollón, chief product officer at VentureCrowd, told VentureBeat that “agents reason against whatever data they can access at runtime” and can be confidently wrong because they’re only basing their knowledge on the context given to them.
“The challenges are rarely about the coding agents themselves; they are about everything around them. It’s a context problem disguised as an AI problem, and it is the number one failure mode I see across agentic implementations.” – Diego Mogollón
Their other roadblock, like many others, was messy data and unclear processes. Similar to context, Mogollón said coding agents would amplify bad data, so the company had to build a well‑structured codebase first.
VentureCrowd’s experience illustrates a broader issue in AI‑agent development: models are not failing the agents; rather, they become overwhelmed by too much context and too many tools at once.
Too Much Context
This stems from a phenomenon called context bloat. When AI systems accumulate more data, tools, or instructions, workflows become more complex. The problem arises because agents need context to work better, but excess context creates noise. More context means more tokens, slower performance, and higher costs.
One way to curb context bloat is through context engineering. It helps agents understand code changes or pull requests and align them with their tasks. However, context engineering often remains an external task rather than being built into the coding platforms enterprises use to build their agents.
How Coding‑Agent Providers Respond
VentureCrowd relied on Salesforce’s Agentforce Vibes to overcome context‑bloat issues. Agentforce Vibes is a coding platform that lives within Salesforce and is available for all plans, starting with the free tier.
Salesforce recently updated Agentforce Vibes to version 2.0, expanding support for third‑party frameworks like ReAct. Most important for companies like VentureCrowd, the update added Abilities and Skills, which can be used to direct agent behavior.
“For context, our entire platform, front‑end and back‑end, runs on the Salesforce ecosystem. So when Agentforce Vibes launched, it slotted naturally into an environment we already knew well.” – Diego Mogollón
Salesforce’s approach doesn’t minimize the context agents use; rather, it helps enterprises ensure that context stays within their data models or codebases. Abilities define what agents want to accomplish, and Skills are the tools they will use to get there.
Other coding‑agent platforms manage context differently. For example:
- Claude Code and OpenAI’s Codex focus on autonomous execution, continuously reading files, running commands, and expanding context as tasks evolve.
- Claude Code includes a context indicator that compacts context when it becomes too large.
Across these approaches, the consistent pattern is that most systems manage growing contexts for agents rather than limiting them. As workflows become more complex, controlling costs, latency, and reliability grows harder.
Mogollón said his company chose Agentforce Vibes not only because a large portion of their data already lives on Salesforce—making integration easier—but also because it allows them to control more of the context they feed their agents.
What Builders Should Know
There’s no single way to address context bloat, but the pattern is clear: more context doesn’t always mean better results.
Enterprises should:
- Invest in context engineering to structure and prioritize the information agents receive.
- Experiment with context‑constraint strategies that suit their comfort level—deciding what to include and, importantly, what to leave out.
By thoughtfully managing context, organizations can mitigate overload, reduce costs, and improve the reliability of AI coding agents.